As argued by [1]}, \(\epsilon _{\textrm {on}}(t)\)  governs the performance of on-\(Q\)  algorithms like DAgger [2]}, \(\epsilon _{\textrm {off}}(t)\)  the performance of off-\(Q\)  algorithms like behavioral cloning [3]}, and \(\epsilon _{\textrm {rew}}(t)\)  the performance of reward-matching algorithms like MaxEnt IRL [4]} and GAIL [5]}. With these definitions laid out, we can now prove how well each class of algorithms handles unobserved contexts.
